Increasing access to palliative care for patients with advanced cancer of African and Latin American descent: a patient-oriented community-based study protocol
Bibliographic record
Abstract
BACKGROUND: Cancer disparities are a major public health concern in Canada, affecting racialized communities of Latin American and African descent, among others. This is evident in lower screening rates, lower access to curative, and palliative-intent treatments, higher rates of late cancer diagnoses and lower survival rates than the general Canadian population. We will develop an Access to Palliative Care Strategy informed by health equity and patient-oriented research principles to accelerate care improvements for patients with advanced cancer of African and Latin American descent. METHODS: This is a community-based participatory research study that will take place in two Canadian provinces. Patients and community members representatives have been engaged as partners in the planning and design of the study. We have formed a patient advisory council (PAC) with patient partners to guide the development of the Access to Palliative Care Strategy for people of African and Latin American descent. We will engage100 participants consisting of advanced cancer patients, families, and community members of African and Latin American descent, and health care providers. We will conduct in-depth interviews to delineate participants' experiences of access to palliative care. We will explore the intersections of race, gender, socioeconomic status, language barriers, and other social categorizations to elucidate their role in diverse access experiences. These findings will inform the development of an action plan to increase access to palliative care that is tailored to our study population. We will then organize conversation series to examine together with community partners and healthcare providers the appropriateness, effectiveness, risks, requirements, and convenience of the strategy. At the end of the study, we will hold knowledge exchange gatherings to share findings with the community. DISCUSSION: This study will improve our understanding of how patients with advanced cancer from racialized communities in Canada access palliative care. Elements to address gaps in access to palliative care and reduce inequities in these communities will be identified. Based on the study findings a strategy to increase access to palliative care for this population will be developed. This study will inform ways to improve access to palliative care for racialized communities in other parts of Canada and globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".